A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications

• URL: http://arxiv.org/abs/2603.04353v1
• Date: Wed, 04 Mar 2026 18:19:35 GMT
• Title: A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications
• Authors: Ozan Aygün, Vincenzo Norman Vitale, Antonia M. Tulino, Hao Feng, Elza Erkip, Jaime Llorca,
• Abstract summary: Next-generation networks aim to provide performance guarantees to real-time interactive services.<n>The goal is to reliably deliver packets with strict deadlines imposed by the application.
• Score: 16.03353922224779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Next-generation networks aim to provide performance guarantees to real-time interactive services that require timely and cost-efficient packet delivery. In this context, the goal is to reliably deliver packets with strict deadlines imposed by the application while minimizing overall resource allocation cost. A large body of work has leveraged stochastic optimization techniques to design efficient dynamic routing and scheduling solutions under average delay constraints; however, these methods fall short when faced with strict per-packet delay requirements. We formulate the minimum-cost delay-constrained network control problem as a constrained Markov decision process and utilize constrained deep reinforcement learning (CDRL) techniques to effectively minimize total resource allocation cost while maintaining timely throughput above a target reliability level. Results indicate that the proposed CDRL-based solution can ensure timely packet delivery even when existing baselines fall short, and it achieves lower cost compared to other throughput-maximizing methods.

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